Overhead imaging studies of a surface below may be hampered by the presence of cloud formations. Understandably, thick clouds between an observation point and the area of interest under observation can conceal objects or features in the area of interest. Potentially worse in some cases is the presence of thinner cloud formations that do not entirely occlude the surface, but may reduce the contrast of surface features and change the derived surface spectral reflectance signature with resulting impact on information products such as spectral vegetation indices. Presence of thin cloud formations, such as cirrus clouds, can skew the analysis of such surface features by causing researchers to confuse presence of cloud features for features or changes in the surface region of study. For example, FIG. 1A shows a representative image 100 of a surface area under study. Merely looking at the image, it may be difficult to determine which aspects of the image are surface features 110 and which aspects are cloud features 120. Further, even though not visible to the naked eye observing an image, or to the eye of a person examining such an image, even sub-visible cloud formations can significantly degrade quantitative spectral analyses of an area being imaged.
Because the presence of cloud formations can interfere with the accuracy of overhead imaging studies, methodologies have been developed to detect the presence of cloud formations so that accuracy of surface studies will not be undermined by undetected cloud patterns. One approach is to use “clear-sky” spectral or reflectance maps of the areas of interest to detect the presence of clouds. By comparing the clear-sky maps with current imaging data, large-area spectral or reflectance changes may signal the presence of cloud cover. This approach involves successfully collecting, verified clear-sky imaging data of the area of interest. The clear-sky maps typically are created using thermal infra-red measurements to determine the presence of cloud formations. Most cloud formations, including high altitude such as cirrus clouds made up of ice crystals present a distinct, differentiable thermal signature. If thermal data indicates the presence of cirrus or other clouds in an area of study, it will be understood which portions of the image data are affected by the presence of clouds. Thus, analysis of the area of interest will not be distorted by the presence of undetected cloud formations.
FIG. 1B shows a “cloud mask” 150 derived using conventional techniques to show the cloud features 120 in the original image 100 of FIG. 1A. Absent the cloud mask 150, it can be appreciated that it might have been easy to confuse edges of cloud patterns 120 with surface features 110.
Unfortunately, as is readily appreciated, collection of thermal-infra red data requires equipment capable of gathering thermal-infrared data. In the realm of orbital satellites, integrating such equipment into the satellite increases cost. Additional telemetry involved in making use of such data also is resource-intensive and costly.
Even where such clear-sky data are available, continual accurate analytical comparison of archival clear-sky data with currently-captured imaging data is needed to ensure that the captured data represents suitably accurate, cloud-free images. Determination of whether the imaging data is suitably cloud-free is a significant concern. If it is not accurately determined whether captured images are suitably cloud-free, it may be necessary to arrange for the areas of interest to be re-imaged. Analysts who desire to use images from an image archive need to be assured that the image data is sufficiently cloud-free to be worthy of acquisition and use in their research. In addition, before quantitative analysis tools are applied to analyze the imaging data, the imaging data must be determined to be suitably cloud-free to ensure that the resulting quantitative analyses will be correct. Alternatively, algorithms may be applied to correct for thin cirrus cloud effects over portions of images affected only (no visible lower cloud) by thin cirrus cloud of reflectance below some arbitrary threshold.
Beyond differentiating terrain covered by clouds, a further challenge in evaluating spectral or reflectance data is to identify shadowed terrain. Shadow-covered terrain may not be easily differentiated from bodies of water, vegetation-covered areas having a naturally dark color, surfaces darkened by rain or surface water, and other areas presenting a relatively lower reflectance. Shadow-covered terrain potentially could corrupt results of an imaging study just as presence of undetected cloud cover could corrupt an imaging study. Performing such differentiation automatically, without involving excessive computing resources is highly desirable. Existing techniques involve computing intensive techniques using neural networks, image fusion, spectral end member analyses and transforms, radiance spatial gradient and continuity analyses, and other resource-intensive processes. Moreover, even such resource-intensive processes may involve significant human expert involvement in “teaching” the system to differentiate between shadowed ground, wet ground, wetlands, or other similar features.
Thus, there is an unmet need in the art for a method for determining presence of clouds and sub-visible clouds in aerial imaging data not involving use of special thermal infrared sensing equipment or the data collected by such equipment.